{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00188566","sets":["6504:9465:9481"]},"path":["9481"],"owner":"6748","recid":"188566","title":["階層ディリクレ過程による動作クラス数推定を導入したGP-HSMMによる連続動作からの基本動作抽出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-03-13"},"_buckets":{"deposit":"cecbe5a8-4a8b-43f7-a959-571bc7a9fb4a"},"_deposit":{"id":"188566","pid":{"type":"depid","value":"188566","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"階層ディリクレ過程による動作クラス数推定を導入したGP-HSMMによる連続動作からの基本動作抽出","author_link":["427891","427888","427893","427890","427892","427889"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"階層ディリクレ過程による動作クラス数推定を導入したGP-HSMMによる連続動作からの基本動作抽出"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2018-03-13","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"},{"subitem_text_value":"電通大"},{"subitem_text_value":"統数研"},{"subitem_text_value":"お茶の水女子大"},{"subitem_text_value":"電通大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/188566/files/IPSJ-Z80-6M-03.pdf","label":"IPSJ-Z80-6M-03.pdf"},"date":[{"dateType":"Available","dateValue":"2018-05-07"}],"format":"application/pdf","filename":"IPSJ-Z80-6M-03.pdf","filesize":[{"value":"624.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"a796b578-94af-4c84-b2b0-acf45baaa3dc","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"長野, 匡隼"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 友昭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"長井, 隆行"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"持橋, 大地"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 一郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"金子, 正秀"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では,ガウス過程(GP)を出力確率分布とした隠れセミマルコフモデル(HSMM)により,連続的な身体動作を教師なしで単位動作に分節化する.さらに,階層ディリクレ過程(HDP)を導入することで,分節化と同時に単位動作の種類数も同時に推定する.提案手法は,GPから単位動作が生成され,それらが結合されることで,動作全体が生成されるとことを仮定した生成モデルである.単位動作の長さとクラスを,Forward filtering-Backward samplingにより推定し,連続動作の分節・分類が可能となる.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"96","bibliographic_titles":[{"bibliographic_title":"第80回全国大会講演論文集"}],"bibliographicPageStart":"95","bibliographicIssueDates":{"bibliographicIssueDate":"2018-03-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"id":188566,"updated":"2025-01-20T01:57:48.237590+00:00","links":{},"created":"2025-01-19T00:54:42.109196+00:00"}